Web: http://arxiv.org/abs/2209.08617

Sept. 20, 2022, 1:13 a.m. | Qing Jin, Zhiyu Chen, Jian Ren, Yanyu Li, Yanzhi Wang, Kaiyuan Yang

cs.CV updates on arXiv.org arxiv.org

Processing-in-memory (PIM), an increasingly studied neuromorphic hardware,
promises orders of energy and throughput improvements for deep learning
inference. Leveraging the massively parallel and efficient analog computing
inside memories, PIM circumvents the bottlenecks of data movements in
conventional digital hardware. However, an extra quantization step (i.e. PIM
quantization), typically with limited resolution due to hardware constraints,
is required to convert the analog computing results into digital domain.
Meanwhile, non-ideal effects extensively exist in PIM quantization because of
the imperfect analog-to-digital interface, …

arxiv memory network neural network processing quantization systems

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